EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Yuxuan Sun, Sheng Zhou, Jie Xu

TL;DR
This paper introduces a novel energy-aware mobility management scheme for mobile edge computing in ultra-dense networks, optimizing delay and energy consumption without future system information, and handling dynamic BS states.
Contribution
It develops an online, user-centric mobility management algorithm based on Lyapunov and bandit theories, considering migration costs and BS on/off dynamics, with proven bounded performance deviations.
Findings
Achieves near-optimal delay performance
Satisfies user energy constraints
Handles dynamic BS on/off states
Abstract
Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · IoT and Edge/Fog Computing · Advanced Wireless Network Optimization
